SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection

ArXi:2605.21186v1 Announce Type: new Interpretability in object detection provides crucial confidence for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods often suffer from blurred foreground boundaries and diffuse feature attribution due to the extreme sparsity of target morphological features and severe interference from complex backgrounds. Such limitations hinder the provision of logically coherent morphological evidence. To bridge this gap, we propose a novel eXplainable AI (XAI) framework, SAM-Sode.